Organizations today are continually increasing their use of predictive analytics to more accurately predict their business outcomes, to improve business performance, and to increase profitability. Common and yet also highly strategic predictive modeling applications include fraud detection, rate making, credit scoring, customer retention, customer lifetime value, customer attrition/churn, and marketing response models.
Predictive analytics generally refer to techniques for extracting information from data to build a model that can predict an output from a given input. Predicting an output can include predicting future trends or behavior patterns. As a result of applying analytics, organizations can better understand business needs and issues, discover causes and opportunities, predict risk levels and events, take steps to prevent risks and events, and perform other similar activities that are beneficial to the organization.
Customer service agents interact with potential customers, clients, or purchasers, i.e., prospects, frequently. During the interaction, useful information can be gathered about the prospect, such as personality type, demographic information, amount of interest, likelihood of sale, etc. Current methods and systems do not use this gathered information to predict how the prospect will behave, or to tailor future interactions with the prospect. Accordingly, improved methods and systems are needed.